Artificial intelligence is hugely popular now, as it offers all-new functionality and automation capabilities. However, AI and machine learning require plenty of training and refinement to get the most out of them. Data plays a pivotal role in this, as feeding your algorithm usable datasets boosts its performance and makes it more versatile.
Today, we’d like to discuss the best practices for using data in AI/ML development based on the experience of S-PRO. Our team has created many AI-centric solutions and knows all about making the most of data to get the best results. With this guide, you get an inside peek at our expertise.
Data Usage in Development: When and How
You can break down the use of data into several stages, as it can be useful throughout a product’s lifecycle. From the moment you make a minimum viable product to the post-release updates, data will help strengthen the quality of your solution. Let’s break down these stages.
Initial Training
As you design the solution and lay its foundation, a team should be dedicated to sourcing varied data—text, media, full-on databases—and prepping it for training. This means removing errors and irrelevant data and diversifying your data sources. The latter is important because it provides more variety, allowing your system to improve its predictive functions and widen its scope.
Measuring Performance
Once you have a complete build, test it and collect analytics. Then, you can assess whether your app meets your standards. Metrics can include performance, accuracy, and scalability. Analyzing that data isn’t just about confirming quality, though, but also about building a roadmap for the changes you need to make to the product.
Monitoring and Refining
As your product launches and, hopefully, thrives, you can use performance data to assess its state and then feed it more datasets to improve the results of its work. You get a more effective AI solution by continuously finding new data sources and changing what the system can do.
How to Boost Data Efficiency in ML Development
Now that you know when to apply data, let’s talk about how to do it optimally. These are just simple ways to apply best practices in your development process.
Keep Data Clean
While there is a mindset of “more data = better,” that’s not necessarily true. The main thing you need to strive for is data cleanliness, filtering out low-quality and irrelevant data. Doing so means your system isn’t cluttered with junk datasets and only learns important points. This enhances its ability to work as a predictive, responsive, and interactive product.
Pick the Right Storage
You want data to be readily available and fed into your system at a good pace, which means preventing bottlenecks and downtime. Similarly, indexing your storage space to have a neat and tidy structure is just as important, both for the system and for manual reviewers.
Challenges in Data Use for AI/ML Development
Data is crucial for AI development, so it’s no wonder that it has some potential issues. Here’s how to navigate them.
Format Compatibility Issues
It’s best to keep training data uniform, which means structuring and segmenting it and using formats that work best within your system. An optimal approach is to use low-size, high-quality formats that are popular enough to be compatible with most solutions.
Lack of Source Diversity
Finding training data isn’t as simple as just downloading some random databases, it needs to be useful for your product’s purposes, and it must be legally accessible. If a product is built using data from only one source, it’s not likely to have great forecasting power or be able to give insights. Prioritize finding multiple sources for your datasets and keeping them distinct.
In Conclusion
You now fully understand the value of data for an AI app and guidance on how to apply it for maximum effect. With ways to skirt around typical challenges associated with data use for machine learning, you can create a really powerful AI-based product. But if you want to take things a step beyond, it’s important to have a skilled team on your side.
S-PRO has years of experience with AI/ML solutions, crafting custom apps and systems with well-trained algorithms. We know the importance of data, thorough testing, and continuous support. If you want to ensure that your AI development is in good hands and that your dev team applies the best practices for data use, look no further.
We can start our collaboration with a quick consultation whenever you’re ready.
